Clothing capture has been explored as a source of geometry for garment modeling. Previous work on this topic can be divided into multi-view approaches and single-view approaches. Multi-view approaches use a multi-camera system or a 3D scanner to capture and reconstruct the shape of the clothing. While the first systems doing this used special devices imaging the cloth in isolation [1]}, the seminal work by Stoll et al. stoll2010animatable estimated cloth simulation parameters from multiview human motion. These systems focus on geometry rather than appearance.
In order for the captured clothing shape to be ready for use in downstream tasks, much effort has been devoted to registration, i.e, the representation of the garment geometry by a consistent mesh topology in a temporal sequence [2]}, [3]}, [4]}, [5]}. Some work [6]}, [7]}, [8]} also estimates the underlying body pose and shape from the clothed human reconstruction. In this paper, multi-view clothing capture provides data to train our appearance modeling network.
